LocalDPO: Teaching Video AIs to Sweat the Small Stuff

Kari Jaaskelainen

08 Jan 2026 — 1 min read

Teaching video AIs to sweat the small stuff

Text-to-video models often miss human-preferred details or waste compute learning from vague, whole-video feedback. LocalDPO, a new training recipe, targets the exact frames and regions that need improvement—no human labels or extra critic model required.

How it works: Use a real, high-quality video as the “good” example, then corrupt small space–time patches to create a “bad” version. Train the model to prefer fixes only in those corrupted spots.
Why it matters: Local, region-aware feedback speeds up learning, sharpens details, and boosts temporal coherence—without multi-sample rankings or extra inference passes.
Results: On Wan2.1 and CogVideoX, LocalDPO improves visual fidelity and human preference scores over other post-training methods.

Bottom line: smarter, fine-grained alignment for more natural, stable videos from your prompts.

Paper: https://arxiv.org/abs/2601.04068v1

Paper: https://arxiv.org/abs/2601.04068v1

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Teaching video AIs to sweat the small stuff

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